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Analysis of Student Course of Study using Data Mining Techniques

Analysis of Student Course of Study using Data Mining Techniques

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DEDICATION

This research material, titled “Analysis of Student Course of Study using Data Mining Techniques” is dedicated to God for His boundless grace and guidance. It is also a tribute to all computer enthusiasts whose contributions made my research journey smoother and enriched my documentation process, making the experience truly fulfilling.




ACKNOWLEDGEMENT

I am profoundly grateful to everyone who contributed to the successful completion of this project. I am especially grateful to my Supervisor (Name), the Head of Department (Name), and the Lecturers in the Department of Computer Science (CS) for their invaluable guidance and support. I also acknowledge the contributions of authors and scholars whose works on Analysis of Student Course of Study using Data Mining Techniques provided essential insights. Special thanks go to my study area (and any funding organizations, if applicable) for their financial assistance. I am equally thankful to stakeholders, including mentors, teachers, and colleagues, for their encouragement and support. Finally, I deeply appreciate my family and friends for their patience and unwavering support throughout this journey. Your contributions have been instrumental in making this research a reality.




PRELIMINARY PAGES


CHAPTER ONE

INTRODUCTION


    CHAPTER TWO

    LITERATURE REVIEW

    • 2.1 Introduction

    CHAPTER THREE

    SYSTEM ANALYSIS AND DESIGN

    • 3.1 Methodology Adopted
    • 3.1.1 Problem Identification Using SSADM
    • 3.2 Analysis of the Existing System
    • 3.2.1 Dataflow of the Existing System
    • 3.2.2 Disadvantages Of The Existing System
    • 3.2.3 Weakness of the existing System
    • 3.3 Feasibility Study
    • 3.3.1 Economic Feasibility
    • 3.3.2 Technical Feasibility
    • 3.3.3 Operational Feasibility
    • 3.4 Analysis of the Proposed System
    • 3.4.1 Data Flow Diagram of the Proposed System
    • 3.4.2 Advantages of the Proposed System
    • 3.4.3 Justification of the Proposed System
    • 3.5 Functional Requirements
    • 3.5.1 Use Case Diagram Of The Admin / User Privileges
    • 3.6 Data Requirements
    • 3.7 High Level Model of the Proposed System

    CHAPTER FOUR

    SYSTEM DESIGN AND IMPLEMENTATION

    • 4.1 Objectives of the Design
    • 4.2 Cohesion and Decomposition High level Model
    • 4.3 Control Center / Overall Dataflow Diagram
    • 4.3.1 Proposed System Operation Flowchart
    • 4.4 System Specification and Design
    • 4.4.1 Input and Output Specification
    • 4.4.2 Database Specification and Design
    • 4.4.3 Data Dictionary
    • 4.5 Choice and Justification of Programming Language
    • 4.6 Program Documentation
    • 4.7 Implementation Techniques
    • 4.7.1 System Testing
    • 4.8 Programming Module Specification
    • 4.8.1 Installation
    • 4.9 Computer Hardware Minimum Requirement
    • 4.10 Software Requirement
    • 4.11 Personnel / User Training
    • 4.12 File Maintenance Module

    CHAPTER FIVE

    SUMMARY, CONCLUSION AND RECOMMENDATION

    • 5.1 Introduction
    • 5.2 Summary
    • 5.3 Conclusion
    • 5.4 Recommendation

    REFERENCES

    APPENDIX A - “SOURCE CODE”

    APPENDIX B - “OBJECT PROGRAM”



    Analysis of Student Course of Study using Data Mining Techniques



    Introduction

    1.1 Background Of The Study

    In recent years, the technology of database has become more advanced where large amount of data is required to be stored in the databases. Data mining then attract more attention to extract valuable information from the raw data that institution can use for decision-making process. It applies modern statistical and computation technologies to expose useful information hidden within the large database to remain competitiveness among educational field, the institution need deep and enough knowledge for a better assessment, evaluation, planning and decision-making. Data mining helps institution to use their current reporting capabilities to discover and identity the hidden patterns in database and hence can be used to predict performance of the student.

    Data mining can be viewed as a result of the natural evolution of information technology because before 1960 when database and information technology had not evolved, analysis of data was basically the primitive file processing which would not give the appropriate useful information despites the huge amount of time consumed. The evolutionary path of data mining has been witnessed in the database industry in the development of the following database and information technology.

    • Data collection and data creation
    • Data management (including data warehouse and data preparation)
    • Data analysis and understanding (involving data mining and data interpretation)

    Moreover, data mining is also known as knowledge discovery in large database (KDD). Consequently, data mining consist of more than collecting and managing data; it also includes analysis and predictions. Important decision are often made based not on the information rich data stored in database but rather on decision maker€™s institution, simply because maker does not have the tools to extract the valuable knowledge embedded in the vast amount of data.


    1.2 Statement of the problem

    It is not feasible for people to analyze great amounts of data without the assistance of appropriate computational tools. Therefore, the development of tools of an automatic and intelligent nature becomes essential for analyzing, interpreting, and correlating data in order to develop and select strategies in the context of each application. To serve this new context, the area of Knowledge Discovery in Databases (KDD), came into existence with great interest within the scientific, industrial, and commercial communities. The popular expression €œData Mining€ is actually one of the stages of the Discovery of Knowledge in Databases. The term €œKDD€ was formally recognized in 1989 in reference to the broad concept of procuring knowledge from databases. One of the most popular definitions was proposed in 1996 by a group of researchers. According to Fayyad, et al. (1996): €œKDD is a process with many stages, non-trivial, interactive, and iterative, for the identification of comprehensible, valid, and potentially useful patterns from large data sets€. It is of utmost desire to extract valuable information from large databases.

    This research work therefore addresses the intelligent prediction of students€™ course of study in higher institution based on the historical student academic data. This will facilitate better performance of students in high institutions.


    1.3 Aim and Objectives of the Project

    1.3.1 Aim

    The aim of the research work is to develop a computer application software that will be able to predict student course of study in higher institution using classification algorithm.

    1.3.2 Objectives

    The following are the set of objectives addressed by the project work:

    1. To develop and populate student academic database
    2. To develop a computer application program that will be able to mine knowledge from the students academic database using Classification algorithm.
    3. To predict student course of study according to their Post UTME cutoff.
    4. To reduce the rate at which student admission is fortified.

    CHAPTER TWO

    2.0 Literature Review

    2.1 Introduction

    This chapter focuses on the review of related literature. A literature review includes the current knowledge as well as theoretical and methodological contributions to a particular topic. It documents the state of the art with respect to the topic you are writing. It surveys the literature in the topic selected. In this research work the literature review includes the conceputal review, theoretical framework, the review of related literature …

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